Introducing the Triple Wash Method for AI prompts

Now you know which scores represent "Quality" in your AI agent. But next: Applying it to every AI call in production? That’s the real game. You can’t check thousands of user interactions manually.
You need automation. 3 fundamental ways: 1. Human Annotation → Ground truth. Small sample, deep accuracy. 2. Rule-based Checks → Black-and-white. Fast. Cheap. Every call. 3. LLM-as-a-Judge → Scales nuance (e.g. helpfulness, relevance). Combine all 3 → Continuous, reliable, scalable evals. That’s how you stop hoping your AI works… and know it does. Diving into AI Observability & Evals (5/6) #AIObservability #Tracing #LLM #AI

RAG: The Future of Reliable AI AI is powerful, but sometimes it gets the facts wrong. That’s where Retrieval-Augmented Generation (RAG) makes the difference. Instead of relying only on guesses, RAG connects to trusted data sources to provide accurate, secure, and reliable answers — every time. ✅ Reduces AI hallucinations ✅ Speeds up decision-making ✅ Builds long-term trust in AI With RAG, you’re not just using AI — you’re using reliable AI. 🔗 Say goodbye to wrong answers. Say hello to confidence. #AI #ArtificialIntelligence #RAG #RetrievalAugmentedGeneration #ReliableAI #TrustworthyAI #FutureOfAI #MachineLearning #DeepLearning #GenerativeAI #AITrends #AITechnology #AIFuture #AIForBusiness #BusinessIntelligence #DecisionMaking #DataDriven #Innovation #TechInnovation #DigitalTransformation #NextGenAI #SecureAI #TechForGood

AI and Qual Analysis: How much is it really scaling? AI has certainly changed the way we handle qualitative data. A lot of transcripts that once felt unmanageable can now be processed in hours. You no longer need to build exhaustive coding frames or Analysis plans. Just start asking questions. But here’s the catch: 1. AI often over-summarizes. The nuance, the edge cases, the contradictions the parts that actually spark insight get flattened. 2. Context blindness remains. Sarcasm, cultural nuance, implicit meaning still slip through the cracks. 3. With Generic purpose LLMs, there’s no traceability back to verbatims and researchers cant trust the output. The real opportunity is this: let machines crunch the haystack, but let humans make sense of the needles. #AI #MarketResearch #CustomerInsights #Innovation

With great data comes great insights. Even the most sophisticated AI models can’t deliver reliable insights without high-quality data and proper processing. That means standardizing formats, correcting gaps… Clean and well-prepared data form the foundation of any successful fuel efficiency program. If you’re curious about how AI transforms raw data into fuel savings, here is an insightful reading: https://lnkd.in/eqhwQE53 #DataPipeline #FuelEfficiency #DataQuality #SkyBreathe #OperationExcellence #AI

Do you have AI integrated into your company’s product or internal tooling? If not, you are missing out. If you want to stay ahead of the competition, AI is a must. You can use it to reach unprecedented levels of speed and efficiency, be it through AI-generated insights based on real data, or workflow automation through AI agents. This goes way beyond simple automation to create intelligent systems that learn, adapt, and aid in data-driven decision-making. If it all sounds interesting, we can handle the AI integration for you. We leverage state-of-the-art models like GPT-5 or Claude 4 to set our clients up for success. #ArtificialIntelligence #AI #analytics #ai_agent #claude #gpt

🤖 AI isn't the future anymore — it's the present. From smarter workflows to data-driven insights, Artificial Intelligence is rapidly becoming the backbone of competitive advantage. This post from Canduit captures how integrating AI into products and internal tools can unlock efficiency, agility, and innovation. The question isn't “Should we adopt AI?” — it's “How fast can we scale with it?” 🚀 #ArtificialIntelligence #AI #analytics #ai_agent #Automation #Innovation #DataDriven #AIagents #GPT #Claude #BusinessGrowth

RAG vs. Fine-Tuning: It's one of the most debated topics in AI. Here’s a simple breakdown of when to use each. Confused about whether to use RAG or Fine-Tuning for your AI project? Let's clear it up with a practical guide. 🔵 Use RAG (Retrieval-Augmented Generation) when you need to: Access real-time or frequently changing information. Eliminate model 'hallucinations' by grounding it in facts. Cite specific sources for its answers. Implement a solution quickly and cost-effectively. Think: Factual Knowledge & Accuracy. 🟠 Use Fine-Tuning when you need to: Teach the AI a very specific, nuanced style, tone, or format. Change the core behavior or 'personality' of the model. Embed specialized knowledge that is static and stylistic. Handle complex, domain-specific instructions. Think: Specialized Skills & Behavior. They aren't mutually exclusive, but knowing where to start is key to building effective and reliable AI. Did this clear things up for you? What other AI topics are you debating? Let's discuss in the comments! 👇 #AI #TechDebate #ArtificialIntelligence #RAGvsFineTuning #MachineLearning

A recent MIT report says 95% of AI projects are failing. Let me add a little spice to that statistic. Most of these projects aren’t failing because of AI; they’re failing because our expectations and workflows are flawed. If your processes are messy, your data is scattered, your approvals are still stuck in 2003, and your spreadsheets look like archaeological dig sites… AI won’t save you. It’ll scale that mess, search every dusty corner of the internet to validate your chaos, and present it like a gift (just not the kind you wanted). AI is built for efficiency, not wisdom. It won’t fix broken systems (not yet). The old rule still applies: garbage in, garbage out. So before handing our tasks to AI, maybe we should take a closer look at our handover notes. Curious to hear from you guys, how are you applying AI in real-world workflows? What’s actually working? #AI #DigitalTransformation #WorkflowMatters #DataQuality #ArtificialIntelligence #EfficiencyNotMagic

Lately, I’ve noticed something odd: some AI assistants feel less sharp than before. Technically, large AI models don’t learn from every single prompt in real time. But they do adapt — through conversation context, retrieval systems, and periodic retraining on aggregated user interactions. That means our collective behavior matters. If most inputs are shallow or noisy, over time the system may start reflecting that back to us. After wrestling with an issue the AI couldn’t resolve, I had to interrupt and guide it toward the correct solution. Curious, I asked: “Do you actually learn from me?” It replied: 💬 “Your feedback creates lasting improvements in how I help everyone, not just you. It’s exactly this kind of real-world correction that makes AI assistants more effective over time.” This made me wonder: are we gradually teaching AI to be smarter — or just to be average? #ArtificialIntelligence #AIAssistants #GenerativeAI #FutureOfWork

Ever feel like you're not getting the most out of AI? It's not just about having the tools, but knowing how to use them effectively. Think of AI as a business assistant that requires specific instructions to deliver exceptional results. Instead of broad requests, the focus shifts to precise queries that guide the AI to provide actionable outputs. It's about learning to articulate needs in a way that unlocks AI's full potential. This approach underscores the importance of clarity and precision in all communication. How do you approach leveraging AI in your field? Curious to hear your insights! Unlock Epic Skills Now! Watch the Full Video: https://bit.ly/3HENrJd #PromptEngineering #ArtificialIntelligence #AIforBusiness #MarketingStrategy #TechSkills #BusinessIntelligence